3D Face Recognition Using 3D Alignment for PCA

Russ, T.D.; Boehnen, Christopher; Peters, Tanya

doi:10.1109/cvpr.2006.13

Cited by 59 publications

(52 citation statements)

References 15 publications

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“…One thing to note is that many of these PCA approaches use a range-based (2.5D) representation. The exception is Russ et al [14] who encode the x, y, and z components with PCA yielding a 95% Pd at .001 FAR when matching neutral probes to a single subject neutral gallery set.…”

Section: Related Workmentioning

confidence: 99%

“…The presented method for correspondences builds upon the approach in [14] and mitigates the associated correspondence issues. Addressing these correspondence issues for data containing variations in facial expression is of particular importance because of the significant differences that occur between drastic facial expressions and the neutral reference face used for correspondence.…”

Section: D Point Correspondence/alignmentmentioning

confidence: 99%

“…Therefore, this work builds upon [14] and analyzes the performance of LDA for expression varying data. It is believed that LDA/PCA approaches can be as or more accurate that 3D point matching schemes such as ICP if the 3D correspondence problem is properly addressed.…”

Section: Related Workmentioning

confidence: 99%

“…And although several PCA techniques have been applied to 3D face recognition [14] [15] [16]. [17], these systems have typically fallen below the FRGC top performers.…”

Section: Related Workmentioning

confidence: 99%

“…However, the 3D model was used to address the problem of pose variation for 2D face recognition systems. Also in [14] each input face was first iteratively aligned to a scaled reference face using ICP and subsequently deformed to approximate an input face using a surface normal correspondence procedure. While this deformation worked reasonably well, it can produce erroneous correspondences in surface regions that vary significantly from the input surface in distance or complexity.…”

Section: D Point Correspondence/alignmentmentioning

confidence: 99%

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Secure portal.

Nelson¹

2007

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There is a need in security systems to rapidly and accurately grant access of authorized personnel to a secure facility while denying access to unauthorized personnel. In many cases this role is filled by security personnel, which can be very costly. Systems that can perform this role autonomously without sacrificing accuracy or speed of throughput are very appealing. To address the issue of autonomous facility access through the use of technology, the idea of a "secure portal" is introduced. A secure portal is a defined zone where state-of-the-art technology can be implemented to grant secure area access or to allow special privileges for an individual. Biometric technologies are of interest because they are generally more difficult to defeat than technologies such as badge swipe and keypad entry. The biometric technologies selected for this concept were facial and gait recognition. They were chosen since they require less user cooperation than other biometrics such as fingerprint, iris, and hand geometry and because they have the most potential for flexibility in deployment.The secure portal concept could be implemented within the boundaries of an entry area to a facility. As a person is approaching a badge and/or PIN portal, face and gait information can be gathered and processed. The biometric information could be fused for verification against the information that is gathered from the badge. This paper discusses a facial recognition technology that was developed for the purposes of providing high verification probabilities with low false alarm rates, which would be required of an autonomous entry control system. In particular, a 3-D facial recognition approach using Fisher Linear Discriminant Analysis is described. Gait recognition technology, based on Hidden Markov Models has been explored, but those results are not included in this paper. Fusion approaches for combining the results of the biometrics would be the next step in realizing the secure portal concept.4

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Section: Related Workmentioning

confidence: 99%

Section: D Point Correspondence/alignmentmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…And although several PCA techniques have been applied to 3D face recognition [14] [15] [16]. [17], these systems have typically fallen below the FRGC top performers.…”

Section: Related Workmentioning

confidence: 99%

Section: D Point Correspondence/alignmentmentioning

confidence: 99%

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Secure portal.

Nelson¹

2007

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Three‐dimensional (3d) Anthropometry and Its Applications in Product Design

Niu

2021

Handbook of Human Factors and Ergonomics

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3D Signatures for Fast 3D Face Recognition

Boehnen

Peters

Flynn

2009

Advances in Biometrics

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We propose a vector representation (called a 3D signature) for 3D face shape in biometrics applications. Elements of the vector correspond to fixed surface points in a face-centered coordinate system. Since the elements are registered to the face, comparisons of vectors to produce match scores can be performed without a probe to gallery alignment step such as an invocation of the iterated closest point (ICP) algorithm in the calculation of each match score. The proposed 3D face recognition method employing the 3D signature ran more than three orders of magnitude faster than a traditional ICP based distance implementation, without sacrificing accuracy. As a result, it is feasible to apply distance based 3D face biometrics to recognition scenarios that, because of computational constraints, may have previously been limited to verification. Our use of more complex shape regions, which is a trivial task with the use of 3D signatures, improves biometric performance over simple spherical cut regions used previously [1]. Experimental results with a large database of 3D images demonstrate the technique and its advantages.

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3D Face Recognition Using 3D Alignment for PCA

Cited by 59 publications

References 15 publications

Secure portal.

Secure portal.

Three‐dimensional (3d) Anthropometry and Its Applications in Product Design

3D Signatures for Fast 3D Face Recognition

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